Optimal design of Kaibel dividing wall columns based on improved particle swarm optimization methods

2020 ◽  
Vol 273 ◽  
pp. 123041 ◽  
Author(s):  
Xing Qian ◽  
Shengkun Jia ◽  
Kejin Huang ◽  
Haisheng Chen ◽  
Yang Yuan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Design ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

scholarly journals Anti-synchronization of discrete-time chaotic systems using optimization algorithms

2011 ◽  
pp. 143-147
Author(s):  
Mahdieh Adeli ◽  
Hassan Zarabadipoor
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Control ◽  
Pid Controller ◽  
Chaotic Systems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Modified Particle Swarm Optimization

In this paper, anti-synchronization of discrete chaotic system based on optimization algorithms are investigated. Different controllers have been used for anti-synchronization of two identical discrete chaotic systems. A proportional-integral-derivative (PID) control is used and its parameters is tuned by the four optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), modified particle swarm optimization (MPSO) and improved particle swarm optimization (IPSO). Simulation results of these optimization methods to determine the PID controller parameters to anti-synchronization of two chaotic systems are compared. Numerical results show that the improved particle swarm optimization has the best result.

Structure Optimal Design for Portable Exoskeleton Using Improved Particle Swarm Optimization

2012 ◽  
Vol 204-208 ◽  
pp. 4845-4850
Author(s):  
Fang Liu ◽  
Wen Ming Cheng ◽  
Yi Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimal Design ◽  
Particle Swarm ◽  
Lower Extremities ◽  
Legged Locomotion ◽  
Design Parameters ◽  
Position Vector ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Portable exoskeleton is directed at providing necessary support and help for loaded legged locomotion. The kernel of whole mechanical construction of the exoskeleton is lower extremities. The lower extremities consist of exoskeleton thigh, exoskeleton shank, hydraulic cylinder and corresponding joints. In order to find the optimal combination of design parameters of lower extremities, an improved particle swarm optimization algorithm based on simulated annealing is proposed. To improve global and local search ability of the proposed approach, the inertia weight is varied over time, and jumping probability of simulated annealing is adopted in updating the position vector of particles. Experimental results show that the improved algorithm can obtain the optimal design solutions stably and effectively with less iteration compared to the standard particle swarm optimization and simulated annealing; using ANSYS software build finite element model with the optimization result, then analyzes the strength of the model, these stress results verify the of accuracy of the improved particle swarm optimization.

Optimal Design of a Compact Filter for UWB Applications Using an Improved Particle Swarm Optimization

2016 ◽  
Vol 52 (3) ◽  
pp. 1-4 ◽  
Author(s):  
Young-Chun Yun ◽  
Seung-Hun Oh ◽  
Jeong-Hyeok Lee ◽  
Kyung Choi ◽  
Tae-Kyung Chung ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Design ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Uwb Applications ◽  
Compact Filter

An improved particle swarm optimization algorithm and its application in solving forward kinematics of a 3-DoF parallel manipulator

2020 ◽  
pp. 095440622093910
Author(s):  
Shuzhen Zhang ◽  
Xiaolong Yuan ◽  
Paul D Docherty ◽  
Kai Yang ◽  
Chunling Li
Keyword(s):  
Particle Swarm Optimization ◽  
Parallel Manipulator ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Degree Of Freedom ◽  
Forward Kinematics ◽  
Swarm Optimization ◽  
Local Optima ◽  
Improved Particle Swarm Optimization

This paper proposes an improved particle swarm optimization to study the forward kinematic of a solar tracking device which has two rotational and one translational degree of freedom. The forward kinematics of the parallel manipulator is transformed into an optimization problem by solving the inverse kinematics equations. The proposed method combines inertial weight with the iterations number and the distance between current swarm particles and the optimum to improve convergence ability and speed. The novel cognitive and social parameters are adjusted by the inertia weight to enhance unity and intelligence of the algorithm. A stochastic mutation is used to diversify swarm for faster convergence via local optima evasion in high dimensional complex optimization problems. The performance of the proposed method is demonstrated by applying it to four benchmark functions and comparing convergence with three popular particle swarm optimization methods to verify the feasibility of the improved method. The behaviors of the proposed method using variable cognitive and social parameters and fixed value are also tested to verify fast convergence speed of variable parameters method. And further, an application example uses the method to determine the forward kinematics of a three-degree-of-freedom parallel manipulator. Finally, the mechanism simulations model of the parallel manipulator are carefully built and analyzed to verify the correctness of the proposed algorithm in PTC Creo Parametric software. In all cases tested, the proposed algorithm achieved much faster convergence and either improved or proximal fitness values.

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